Computational Methods for Investigating the Genetics of Gene Regulation

研究基因调控遗传学的计算方法

• 批准号: 10708664
• 负责人: William Majoros
• 金额: $ 36.7万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708664
• 关键词: Alleles; Biological Assay; CRISPR interference; Cells; Collaborations; Complex; Computing Methodologies; Data; Development; Diagnosis; Disease; Doctor of Philosophy; Enhancers; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Human Genome; Individual; Methods; Modern Medicine; Mutation; Patients; Positioning Attribute; Regulator Genes; Regulatory Element; Reporter; Science; Statistical Methods; Statistical Models; Therapeutic; Untranslated RNA; Variant; Work; causal variant; design; genetic disorder diagnosis; genetic variant; genome wide association study

Abstract Genetic diagnosis of complex disease is an important challenge in modern medicine. As a majority of GWAS hits implicate noncoding regions of the human genome, regulatory elements such as enhancers have become a major focus in the search for causal mechanisms. This proposal focuses on the development of computational methods for analyzing experimental data relevant to gene regulatory mechanisms and the variants that can perturb them, leading to disease. My lab is well positioned to have a sizeable impact on the experimental science in gene regulation ongoing at Duke and elsewhere, via existing collaborations and memberships in multiple consortia. In particular, my lab has been developing statistical models for detecting allele-specific gene expression in individuals and trios, to identify genes that may be under dysregulation. My lab also continues to develop methods for analyzing genetic variant data from massively parallel reporter assays, which was a focus of my Ph.D. thesis. And my lab has begun developing statistical methods for analyzing CRISPRi perturbations in single-cell data to identify gene-enhancer relationships. We expect these synergistic projects to result in more effective identification of causal variants and a higher diagnosis rate for currently undiagnosed patients of a wide variety of diseases, as well as potential leads toward the design of therapeutics.

抽象的 复杂疾病的基因诊断是现代医学的一个重要挑战。作为 GWAS 的大多数 命中涉及人类基因组的非编码区域，增强子等调控元件已成为 主要关注于寻找因果机制。该提案的重点是计算技术的发展 分析与基因调控机制和变异相关的实验数据的方法 扰乱它们，导致疾病。我的实验室处于有利地位，可以对实验科学产生相当大的影响 通过现有的合作和多个领域的成员资格，杜克大学和其他地方正在进行的基因调控研究 财团。特别是，我的实验室一直在开发用于检测等位基因特异性基因的统计模型 个体和三人组中的表达，以确定可能失调的基因。我的实验室也继续 开发分析来自大规模并行报告基因检测的遗传变异数据的方法，这是一个焦点 我的博士学位论文。我的实验室已经开始开发用于分析 CRISPRi 扰动的统计方法 在单细胞数据中识别基因增强子关系。我们预计这些协同项目将带来更多成果 有效识别因果变异并提高目前未确诊患者的诊断率 各种疾病，以及治疗方法设计的潜在线索。